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REVIEW 3 major objections 4 minor 106 references

Recommending With, Not For: Co-Designing Recommender Systems for Social Good

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Recommender systems aimed at social good should be designed with and by the stakeholders who experience their effects, not merely tested on them as research subjects.

desk verdict A clear, honest position paper that makes a strong normative case for participatory recommendation; the feasibility gap in co-evaluation is real but the paper admits it, and the agenda deserves a serious referee. read the letter →

arxiv 2508.03792 v1 pith:ADVEZLTH submitted 2025-08-05 cs.HC cs.CYcs.IR

Michael D. Ekstrand , Afsaneh Razi , Aleksandra Sarcevic , Maria Soledad Pera , Robin Burke , Katherine Landau Wright This is my paper
classification cs.HCcs.CYcs.IR
keywords recommendersystemsparticipatorydesignco-designsocialgoodco-evaluationmultistakeholderrecommendationhuman-centeredAIevaluationmetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that recommender systems for social good should be co-designed with and by the people who experience their effects, not merely for them as passive test subjects. Today, engineers and platform teams set the goals, choose the metrics, and judge success, so the system embodies the designers' vision of social good. The paper proposes participatory recommendation, in which a participatory design team of users, providers, and other stakeholders shares power across problem definition, design, evaluation, and ongoing monitoring. This matters because a system that claims to advance social good should be accountable to the communities it claims to help.

What carries the argument

The central object is the participatory design (PD) team, a working group in which diverse stakeholders collaborate across the full recommender lifecycle. The mechanism is the participation spectrum running from consultation, through involvement and participation, to full co-production, where participants have equal say over project goals and outcomes. The argument uses this spectrum to judge design-and-evaluation practices: full co-production, not consultation or focus groups, is what participatory recommendation requires, and evaluation metrics and objective functions are treated as design artifacts that the PD team must co-develop rather than receive from experts.

What would settle it

A field study in which stakeholder co-designers are given full authority over a social-good recommender's evaluation metrics, and the resulting metrics are then shown to be less valid than expert-designed ones on the very outcomes the community cares about, would falsify the feasibility premise. More simply, if a co-design process produces no measurable change in the system's goals, metrics, or deployment decisions compared with a designer-only process, the central claim about power sharing would be refuted.

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Extended reading notes

Core claim

The central claim is normative: social-good recommender systems should be co-designed with their stakeholders as full co-designers, not just as user study participants. The paper argues that the entire lifecycle—from defining the problem, to designing objective functions, to interpreting evaluation results—should be a power-sharing collaboration among users, providers, and others affected, with designers contributing technical expertise but not acting as final arbiters. If true, the goals inscribed in such systems would reflect the values of the communities that experience their benefits and harms rather than the platform's alone.

Load-bearing premise

The feasibility premise is that people with widely varying expertise can meaningfully co-design and validate statistically complex evaluation metrics; the paper itself concedes that metrics can be difficult even for experienced recommender designers, and that substantial research is needed to make co-evaluation work.

Editorial extensions

If this is right

  • Social goals for a recommender would be selected by the people it affects, so 'success' could not be defined by the platform alone.
  • Evaluation metrics and experimental designs would be co-created and co-interpreted, requiring new tools that make statistical results legible to non-experts.
  • Designers would shift from final decision-makers to expert contributors, retaining authority only over technical feasibility and consequences.
  • The approach would start with small, well-defined systems and scale by amalgamating many local participatory efforts rather than by one global design.
  • If stakeholders had a real say, user acceptance and trust in the resulting systems could rise, although the paper presents this as a hypothesis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The strongest feasibility risk is that participatory processes could be captured by the most organized stakeholders, substituting new power imbalances for old ones; the paper acknowledges power conflicts but offers no guardrails.
  • A testable implication is that co-designed systems will match community-defined social outcomes better than designer-only systems, which could be tested in a randomized comparison of design processes.
  • The argument implicitly extends beyond recommender systems to search engines and other information access tools, as the paper itself notes, suggesting a broader participatory agenda for information systems.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This position paper argues that recommender systems aimed at social good should be designed by and with their users, providers, and other affected stakeholders as full co-designers, rather than merely as user study participants. The authors review current recommender-system design and evaluation practice, outline the history and principles of participatory design and co-design, and propose a vision of 'participatory recommendation' that spans problem definition, design, evaluation, and ongoing monitoring. The paper develops this vision through three application case studies (fair marketplaces, social media and online safety, and education) and closes with a research agenda listing needed tools, evaluation methods, and political commitments.

Significance. If the central claim is accepted, this paper gives the recommender-systems community a coherent, well-referenced argument for moving beyond user-centered design toward genuine co-production, and it connects the field to the participatory-design tradition in HCI. The paper is notably honest about its own open problems: it explicitly states that much methodological capability 'has yet to be done' (§4) and that 'substantial research is needed' to make co-evaluation work (§4.3). The three case studies ground the argument in concrete domains, and the authors' extensive prior work on fairness and multistakeholder recommendation gives the proposal credibility. The paper's main contribution at this stage is as a framework and agenda rather than a demonstrated method; its significance will depend on whether researchers can supply the feasibility evidence and power-sharing mechanisms it currently lacks.

major comments (3)
  1. [§1, §4, §4.3] The paper's central claim is that participatory collaboration is 'necessary to truly achieve the goals of recommendation for social good' (§4). This necessity claim rests on the feasibility premise that nonexpert stakeholders can meaningfully participate in designing and interpreting statistically complex evaluation metrics. The paper itself concedes that metrics 'can be difficult even for experienced recommender system designers to interpret' and that 'substantial research is needed' to make co-evaluation work (§4.3), and it lists evaluation techniques enabling nonexperts as an unmet research need (§6). Given these concessions, the unconditional 'necessary' claim is not supported. The authors should either (a) condition the claim on a stated feasibility assumption and articulate what minimal evidence would test it, or (b) substantially expand the discussion of existing participatory evaluation efforts (e.g., Smith et al. 2024) that show nonexperts can acquire meaningful evaluative authority. Without this, the prescriptive force of the paper's main thesis is weakened.
  2. [§3.2, §4, §6] The paper acknowledges 'participation-washing' and co-option of participatory work (§3.2) and stresses that PD team collaborations should be 'genuinely power-sharing relationships' (§4), but it offers no mechanism by which power imbalances are actually neutralized. The promised accountability in §6—where people 'hold such efforts accountable'—follows only if the participatory process transfers real decision-rights to affected stakeholders. The paper should address at least one concrete power-sharing mechanism, such as stakeholder veto rights over deployment, independent facilitation, resource redistribution, or the institutional conditions under which such mechanisms might be feasible. As written, the political commitment is stated but the means to prevent capture are not, leaving a gap between the vision and its claimed outcome.
  3. [§5.1, §5.2, §5.3] The case studies in Section 5 are framed as demonstrating how participatory methods 'could impact' recommender design, but none of them actually instantiates full co-production. The Kiva and journalist examples are interview and focus-group studies (§5.1); the social media section describes potential uses of co-design without reporting a completed participatory process (§5.2); and the education example expresses teacher involvement as an aspiration (§5.3). These cases do not provide empirical support for the claim that participation is necessary for social good; they show that stakeholder perspectives diverge from designer assumptions. The authors should explicitly characterize these as illustrative of the need for participation, not as evidence that participatory recommendation is feasible or effective in its full form. If such evidence is not yet available, the paper should say so and identify what future demonstrations would be needed.
minor comments (4)
  1. [Abstract] The phrase 'designedbyandwith' is missing spaces; it should read 'designed by and with'.
  2. [Figure 2] The spectrum labels 'consultation,' 'involvement,' 'participation,' and 'co-production' are defined only in prose; adding a one-sentence example for each level would help readers place recommender-system practices on the spectrum.
  3. [§4.3] The bullet list includes 'Designing and validating evaluation metrics' as a task for the full PD team, immediately after the sentence stating that substantial research is needed to make co-evaluation work; the apparent tension should be resolved by clarifying which aspects of metric design are delegated to experienced researchers as opposed to decided jointly.
  4. [References] The citation to 'Gómez Gutiérrez et al. 2021' appears in the text alongside 'Gómez et al. 2021' with no explanation of the distinction between the two works; a brief parenthetical clarification would avoid confusion.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the paper is a normative argument; its self-citations are scaffolding, and the feasibility gaps it admits are unsupported premises rather than self-referential reductions.

full rationale

Score 2 rather than 0 because the paper leans on several of the authors' prior works (Ekstrand & Willemsen 2016; Burke & Sylvester 2024; Smith et al. 2024; Ekstrand et al. 2024b) as scaffolding, but none of these citations does load-bearing logical work. The paper is a position paper: its central claim that social-good recommender systems should be designed by and with affected stakeholders follows from the normative premise that 'social goals and operationalizations should be developed through participatory and democratic processes that are accountable to their stakeholders' (Abstract and Section 1), not from a fitted parameter or from a self-citation chain. There are no equations, no fitted inputs renamed as predictions, and no uniqueness theorem imported from the authors. The self-citations are pointers to prior arguments and empirical studies (e.g., Smith et al. 2024 on co-designing fairness metrics with providers), and the paper explicitly frames the missing methodology as future work: Section 4 says 'much of the work on adapting and applying participatory design and co-design ... has yet to be done,' and Section 4.3 says metrics 'can be difficult even for experienced recommender system designers to interpret' and that 'substantial research is needed' for co-evaluation; Section 3.2 also acknowledges risks such as 'participation-washing.' These are evidence and feasibility gaps of the sort the skeptic raises, which weaken the argument's actionability but are not circularity. Per the hard rules, missing support is a correctness risk, not a circularity finding, so the analysis does not escalate the score.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no fitted parameters or invented entities. Its central argument rests on normative and domain assumptions about the value and feasibility of participatory co-design, which are stated in the text but not empirically validated.

assumptions (3)
  • domain assumption People who experience the benefits and harms of a recommender system are best positioned to define social good and assess its attainment.
    Stated in Section 1 and Section 6 ('assessed by the people who will be affected'); it is the normative foundation of the argument and is assumed, not demonstrated.
  • domain assumption Full co-production-level participation yields systems that better serve stakeholder-defined social goals than designer-led processes.
    Implicit in Section 4 and Section 6; the paper posits that participatory approaches are 'key' to relational technology but provides no empirical evidence comparing outcomes.
  • domain assumption Non-expert stakeholders can meaningfully participate in designing statistically complex evaluation metrics and reviewing results.
    Required by Section 4.3, which lists metric design as a PD team task; the paper itself notes substantial research is needed to make this possible, so the premise is load-bearing and currently unproven.

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Cite this review

Pith. "Pith review of Recommending With, Not For: Co-Designing Recommender Systems for Social Good." pith.science (2026). https://pith.science/paper/ADVEZLTH

@misc{pith2026250803792,
  author       = {Pith},
  title        = {Pith review of: Recommending With, Not For: Co-Designing Recommender Systems for Social Good},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ADVEZLTH}},
  note         = {Machine review of arXiv:2508.03792}
}
read the original abstract

Recommender systems are usually designed by engineers, researchers, designers, and other members of development teams. These systems are then evaluated based on goals set by the aforementioned teams and other business units of the platforms operating the recommender systems. This design approach emphasizes the designers' vision for how the system can best serve the interests of users, providers, businesses, and other stakeholders. Although designers may be well-informed about user needs through user experience and market research, they are still the arbiters of the system's design and evaluation, with other stakeholders' interests less emphasized in user-centered design and evaluation. When extended to recommender systems for social good, this approach results in systems that reflect the social objectives as envisioned by the designers and evaluated as the designers understand them. Instead, social goals and operationalizations should be developed through participatory and democratic processes that are accountable to their stakeholders. We argue that recommender systems aimed at improving social good should be designed *by* and *with*, not just *for*, the people who will experience their benefits and harms. That is, they should be designed in collaboration with their users, creators, and other stakeholders as full co-designers, not only as user study participants.

Figures

Figures reproduced from arXiv: 2508.03792 by the authors.

Figure 1
Figure 1. Diagram of typical recommender system design and evaluation process. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Spectrum of the level of participation in various research and design paradigms. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.